Journal article
A Data-Driven Bidding Model for a Cluster of Price-Responsive Consumers of Electricity
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Nordea3
CITIES - Centre for IT-Intelligent Energy Systems, Centers, Technical University of Denmark4
This paper deals with the market-bidding problem of a cluster of price-responsive consumers of electricity. We develop an inverse optimization scheme that, recast as a bilevel programming problem, uses price-consumption data to estimate the complex market bid that best captures the price-response of the cluster.
The complex market bid is defined as a series of marginal utility functions plus some constraints on demand, such as maximum pick-up and drop-off rates. The proposed modeling approach also leverages information on exogenous factors that may influence the consumption behavior of the cluster, e.g., weather conditions and calendar effects.
We test the proposed methodology for a particular application: forecasting the power consumption of a small aggregation of households that took part in the Olympic Peninsula project. Results show that the price-sensitive consumption of the cluster of flexible loads can be largely captured in the form of a complex market bid, so that this could be ultimately used for the cluster to participate in the wholesale electricity market.
Language: | English |
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Publisher: | IEEE |
Year: | 2016 |
Pages: | 5001-5011 |
ISSN: | 15580679 and 08858950 |
Types: | Journal article |
DOI: | 10.1109/TPWRS.2016.2530843 |
ORCIDs: | Madsen, Henrik |
Bilevel programming Demand response Electricity markets Inverse optimization SDG 7 - Affordable and Clean Energy Smart grid
Electricity supply industry Estimation Indexes Load modeling Optimization Power demand Programming bilevel programming bilevel programming problem complex market bid estimation data-driven bidding model demand response electricity markets electricity price-responsive consumers exogenous factors inverse optimization inverse optimization scheme marginal utility functions market-bidding problem optimisation power markets price-consumption data pricing wholesale electricity market